sustainability

Article Understanding Degradation and Restoration from the Perspective of Services: A Case Study of the Xilin River Basin in ,

Xuefeng Zhang 1, Jianming Niu 1,2,*, Alexander Buyantuev 3, Qing Zhang 1, Jianjun Dong 1, Sarula Kang 1 and Jing Zhang 1,4

1 College of Life Sciences, Inner Mongolia University, Hohhot 010021, China; [email protected] (X.Z.); [email protected] (Q.Z.); [email protected] (J.D.); [email protected] (S.K.); [email protected] (J.Z.) 2 Sino-US Center for Conservation, Energy, and Sustainability Science, Inner Mongolia University, Hohhot 010021, China 3 Department of Geography and Planning, University at Albany, State University of New York, Albany, NY 12222, USA; [email protected] 4 College of Environment and Resources, Dalian Nationalities University, Dalian 116600, China * Correspondence: [email protected]; Tel.: +86-471-499-2735

Academic Editor: Vincenzo Torretta Received: 25 April 2016; Accepted: 15 June 2016; Published: 24 June 2016

Abstract: Ecosystem services (ESs) and their transformations in northern China play a crucial role in regional sustainability. During the past several decades, grassland degradation has become one of the most important ecological and economic issues in this region. Therefore, understanding the impacts of grassland degradation and restoration on ESs is essential for maintaining ecological resilience and social security of Northern China. Our objective was to explore the relationship between ESs and grassland changes induced by vegetation succession in the Xilin River Basin, Inner Mongolia, China. Using vegetation maps derived from remotely sensed imagery collected in 1983, 1989, 2000, and 2011, we calculated the degree of grassland degradation using the Grassland Degradation Index (GDI). Aboveground biomass (AGB), soil conservation (SC), and water retention (WR) were also estimated to assess ESs for each year. Our results show that: (1) GDI increased during 1983–2000 and decreased during 2000–2011 indicating that after experiencing two decades of severe degradation the grassland has been restored since 2000. (2) AGB and SC were significantly negatively correlated with GDI. Changes in grassland conditions significantly affected WR and SC with both declining during 1983–2000 and increasing afterwards. The increase of SC, however, was slow compared to AGB and WR, which is an indication of time lag in soil restoration. (3) in the middle and lower reaches experienced worse degradation than in the upper reaches. (4) AGB and SC exhibited a synergy, while trade-offs existed between AGB and WR and SC and WR. In summary, significant changes in grassland in the Xilin River Basin over the past three decades affected the dynamics of ESs among which SC and WR require special attention in the future.

Keywords: grassland degradation; ecosystem services; grassland degradation index; Xilin River Basin; Inner Mongolia grassland

1. Introduction Ecosystem services (ESs) are the benefits people obtain from ecosystems [1]. Not only do they provide a multitude of goods and services, including clean air, fresh water, food, fuel, and other services, but they also play a critical role in maintaining and improving human well-being, which is

Sustainability 2016, 8, 594; doi:10.3390/su8070594 www.mdpi.com/journal/sustainability Sustainability 2016, 8, 594 2 of 17 essential for regional landscape sustainability [1–3]. However, over the past 50 years, approximately 60% of the studied ESs have been degraded due to increases in global population and economic growth, especially in drylands [1]. To provide a better understanding of changes of degrading ecosystems and the potential for ecosystem restoration, ESs has been identified as an ecological concept important for policymakers and practitioners in recent years. Grasslands in China cover 41.7% of land area [4], with nearly 80% of them occurring in arid and semiarid regions [5]. During the past several decades, grassland degradation has reduced its ecosystem productivity and and led to sandstorms and desertification making it a significant environmental problem of northern China [6,7]. Grassland degradation, the primary form of land desertification, is defined as the process of retrogressive succession of grassland ecosystems resulting from human activities (, reclamation) and unfavorable natural conditions [6]. The attributes of grassland degradation include the decline of suitability of vegetation for multiple uses (, biodiversity conservation and recreation), the increase of proportion of less palatable plants, the increase of erodability of topsoil, and the decrease of root-zone moisture holding capacity [8,9]. Many studies have examined grassland degradation at local scales in Inner Mongolia, China [10,11], but monitoring grassland degradation with sufficient detail at the regional scale has been lacking [8]. Ground observations of grassland degradation allowed researchers to identify five levels of degradation: non-degraded, lightly degraded, moderately degraded, severely degraded, and extremely degraded [6,12,13]. These studies contributed to the progress of understanding of grassland successions and the practice of grassland restoration. At the same time, modern remote sensing and GIS have become critical tools of monitoring grassland degradation [14]. Grassland degradation can be effectively mapped and monitored with the help of these technologies [15–17]. Yet, most studies are still based on the Normalized Difference Vegetation Index (NDVI) from satellite data [18,19]. Such NDVI data are largely inadequate for understanding changes in structure and composition, as well as other complex ecological processes in degrading grasslands. As a remedy to this problem, the grassland degradation index (GDI) was developed to quantify the degree of degradation and changes of spatiotemporal patterns. The index allows assessing the condition and spatial extent of steppe ecosystems [8,20]. Grassland degradation is a complex process that integrates various aspects, including changes in soil conditions, biodiversity, productivity, and socio-economics [21]. Therefore, comprehensive assessment of grassland degradation and restoration is increasingly important. ESs provide a new perspective for better understanding the process of grassland degradation and restoration. They have been categorized into four types: provisioning, regulating, cultural, and supporting services [1]. The early research on ESs was mainly focused on defining important concepts and terminology [2,22] and service valuation [23,24]. The Millennium Ecosystem Assessment (MA) by the United Nations has increased the worldwide popularity of ESs. With the rapidly growing research on ESs, more attention has been paid to quantitative modeling and mapping of ESs [25–27], analyses of trade-offs and synergies between multiple ESs [28–30], integration of ESs into conservation, restoration, and sustainability science [3,31,32]. It is therefore important to adapt the ESs concept to the understanding of grassland degradation and restoration [32,33]. To date, research on how ESs respond to grassland degradation and restoration efforts have been lacking in arid and semiarid regions [34]. Degradation of grasslands in arid and semiarid regions has accelerated in the recent decades, which requires spatially explicit impact assessment and ESs restoration strategies. This was our motivation exploring the relationship between ESs and grassland changes induced by vegetation succession. In this case study, we selected Xilin River Basin in Inner Mongolia, China, which is one of the most representative typical grasslands of the region. The following problems have been investigated: (1) quantification of spatiotemporal patterns of grassland degradation and ESs of the Xilin River Basin during 1983–2011; (2) analysis of changes and correlations between grassland degradation and ESs; (3) discuss the potential trade-offs and synergies among ESs. This study will provide valuable scientific information for the assessment of grassland restoration and regional landscape sustainability in arid and semiarid regions. Sustainability 2016, 8, 594 3 of 17

Sustainability 2016, 8, 594 3 of 17 2. Materials and Methods 2. Materials and Methods 2.1. Study Area 2.1.The Study Xilin Area River Basin (43˝261N–44˝391N, 115˝321E–117˝121E) is located in central Inner Mongolia 2 AutonomousThe Xilin Region, River Basin China (43°26 (Figure′N–44°391) and′N, occupies 115°32′E–117°12 9786 km′E) is. Fromlocated southeast in central toInner northwest, Mongolia the topographyAutonomous gradually Region, decreasesChina (Figure from 1) aboutand occupies 1500 m 9786 to 902 km2 m. From above southeast sea level. to northwest, The temperate the semi-aridtopography continental gradually climate decreases of the from area about is characterized 1500 m to 902 bym above the annual sea level. temperature The temperate of 1 ˝ semiC–4 ‐˝C. Annualarid continental precipitation climate changes of the from area 350 is mmcharacterized in the southeast by the annual to 250 mmtemperature in the northwest of 1–4 °C. and Annual occurs mainlyprecipitation between changes June and from August. 350 mm The in weather the southeast is cold to and 250 dry mm in in the the winter northwest but warmand occurs and wet mainly in the summerbetween [35 June]. Dominant and August. soil The types weather are chestnut, is cold and chernozem, dry in the winter and aeolianbut warm [36 and]. The wet areain the has summer diverse plant[35]. communities Dominant soil found types throughout are chestnut, much chernozem, of the steppe and region aeolian of northern[36]. The China area has [35 ].diverse Most common plant steppecommunities species arefoundStipa throughout grandis, Stipamuch Kryloviiof the steppe, and Leymusregion of chinensis northern. BeingChina one[35]. ofMost the common Biosphere Reservessteppe byspecies the Man are andStipa Biosphere grandis, Stipa Programme Krylovii, Xilinand Leymus River Basin chinensis is representative. Being one of of the the Biosphere vast steppe ofReserves northern by China the Man (Figure and1 )[Biosphere35]. Programme Xilin River Basin is representative of the vast steppe of northern China (Figure 1) [35].

Figure 1. Location of the study area. Figure 1. Location of the study area.

2.2. Data Collection and Processing 2.2. Data Collection and Processing Data used for calculating GDI in the basin were based on 1983, 1989, 2000, and 2011 grassland vegetationData used maps for produced calculating from GDI Landsat in the basinTM and were MSS based images. on One 1983, set 1989, of Landsat 2000, and MSS 2011 images grassland and vegetationthree sets mapsof Landsat produced TM images from (path Landsat 124/row TM 29, and path MSS 124/row images. 30, cloud One setcover of <10%) Landsat were MSS acquired images andon three 12 July sets 1983, of Landsat5 August TM 1989, images 18 July (path 2000, 124/row and 2 August 29, path 2011, 124/row respectively. 30, cloudAll images cover were <10%) weredownloaded acquired from on 12 the July United 1983, States 5 August Geological 1989, Survey 18 July [37] 2000,and georeferenced and 2 August and 2011, atmospherically respectively. Allcorrected images using were ENVI downloaded 4.7 software from [38] the using United 1:50,000 States topographic Geological maps Survey as the [ 37reference.] and georeferenced Vegetation andclassifications atmospherically were correctedperformed usingusing ENVITWINSPAN 4.7 software (Two‐way [38] Indicator using 1:50,000 Species topographic Analysis) software maps as the[39] reference. derived Vegetationfrom the 120 classifications field sites of were vegetation performed surveys using during TWINSPAN 2009–2011. (Two-way The layers Indicator with Speciesvegetation Analysis) patches software were generated [39] derived using from eCongnition the 120 field8.0 software sites of [40] vegetation with multi surveys‐resolution during 2009–2011.segmentation The approach layers with (set vegetation scale parameter patches to 90) were based generated on these Landsat using eCongnition images, then 8.0 the software vegetation [40 ] withmaps multi-resolution were interpreted segmentation by ArcGIS 10.0 approach software (set [41] scale and expert parameter knowledge to 90) derived based onfrom these published Landsat images,vegetation then themaps vegetation and field maps surveys were [42]. interpreted Five vegetation by ArcGIS subtypes 10.0 software (meadow [41 steppe,] and expert typical knowledge steppe, derivedsand vegetation, from published meadow vegetation and others), maps 17 andformations field surveys and 22 association [42]. Five vegetation groups were subtypes mapped (meadow (Table steppe,1) [42,43]. typical We steppe, calculated sand the vegetation, Normalized meadow Difference and Vegetation others), 17 Index formations (NDVI) and from 22 associationLandsat TM groups and MSS images to derived maps of vegetation cover. The National Oceanic and Atmospheric Sustainability 2016, 8, 594 4 of 17 were mapped (Table1)[ 42,43]. We calculated the Normalized Difference Vegetation Index (NDVI) from Landsat TM and MSS images to derived maps of vegetation cover. The National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) NDVI (1981–1999) and Moderate-Resolution Imaging Spectroradiometer (MODIS) NDVI product (MOD13A2, 2000–2015) in growing season (April–October) were downloaded from the Cold and Arid Regions Sciences Data Center [44] and the NASA [45], respectively. In addition, ASTER Global Digital Elevation Model (ASTER GDEM) data at 30ˆ30 m resolution were downloaded from the Japan Space Systems [46]. Meteorological data, including precipitation and temperature, were obtained from the Inner Mongolia Meteorological Bureau. Lastly, maps of soil type, soil texture, and soil depth, were provided by the Inner Mongolia Grassland Ecosystem Research Station, Chinese Academy of Sciences.

Table 1. Vegetation classification of degradation rankings of plant communities in the Xilin River Basin.

Vegetation Subtypes Formation Types Association Degradation Ranking Meadow steppe Mesophyious forbs meadow steppe L. chinensis + forbs Undegraded L. chinensis + Stipa. spp. Undegraded Leymus chinensis steppe L. chinensis + Artemisia frigida Moderately degraded S. grandis Undegraded Stipa grandis steppe S. grandis + L. chinensis Undegraded S. grandis + Cleistogenes squarrosa Slightly degraded Typical steppe S. krylovii + S. gobica Undegraded Stipa krylovii steppe S. krylovii + L. chinensis Slightly degraded S. krylovii + C. squarrosa Moderately degraded Cleistogenes squarrosa steppe C. squarrosa + A. frigida Heavily degraded Caragana microphylla Caragana microphylla – grass + forbs Heavily degraded thicketization of steppe Pioneer plants in sand dunes Agriophyllum pungens + Corispermum spp. Heavily degraded Sand vegetation Artemisia intramongolica formation A. intramongolica Undegraded C. microphylla scrub C. microphylla – A. intramongolica Undegraded Carex korshinskyi meadow Carex spp. + forbs Heavily degraded Meadow Achnatherum splendens meadow A. splendens Undegraded Farmland, Waters, Saline and alkali land, Others Others Bare land, Urban or residential area, Undegraded Industry and mining land

2.3. Methods

2.3.1. Grassland Degradation Index (GDI) The GDI, developed to detect steppe communities with different degrees of degradation [8], is calculated as follow: 3 Wi ¨ Ai GDI “ i“1 , i “ 0, 1, 2, 3 (1) ř Ak where i denotes 4 levels of degradation (undegraded and slightly, moderately, and heavily degraded, respectively) (Table1), Wi is weights of grassland degradation rank i, Ai is areas of the degraded grassland of rank i, and Ak is the spatial extent of the geographic areas under consideration. Wi is equal to 0, 1, 1.5 and 2.0 for undegraded, slightly degraded, moderately degraded, and heavily degraded steppes, respectively. Ai is calculated from the relationship between vegetation classifications and degradation rankings (Table1) based on the grassland vegetation maps in 1983, 1989, 2000, and 2011. Sustainability 2016, 8, 594 5 of 17

2.3.2. Quantification of Grassland Ecosystem Services (ESs)

Aboveground Biomass (AGB) The primary function of the ecosystem is the provisioning of raw materials [47], so biomass can be used as a measure for raw materials of grassland ecosystems. To assess AGB we adopted the regression model (R2 = 0.47, p < 0.001) developed for our study area using remote sensing and field data [48]:

AGB “ 1101.711NDVI ` 454.638 (2) where NDVI is the Normalized Difference Vegetation Index, calculated as follows:

NIR ´ RED NDVI “ (3) NIR ` RED where NIR and RED correspond to spectral reflectance in the near-infrared band and the red band, respectively.

Soil Conservation (SC) SC is calculated as the difference between potential and actual soil losses computed using the Revised Universal Soil Loss Equation (RUSLE) [49]:

Ap “ R ¨ K ¨ LS (4)

´1 ´1 where Ap is the potential soil loss (t¨ ha ¨ yr ), R is the rainfall erosivity factor (MJ¨ mm¨ ha´1¨ h´1¨ yr´1), K is the soil erodibility factor (t¨ ha¨ h¨ ha´1¨ MJ´1¨ mm´1), LS is the topographic factor.

Ar “ R ¨ K ¨ LS ¨ C ¨ P (5)

´1 ´1 where Ar is the actual soil loss (t¨ ha ¨ yr ), C is the vegetation cover factor, and P is the conservation practices factor. Soil conservation (Ac) can be estimated as follows:

Ac “ AP ´ Ar “ R ¨ K ¨ LS ¨ p1 ´ C ¨ Pq (6)

Water Retention (WR) We used the water retention model to assess WR function of grassland ecosystems. The model is based on the water balance principle, according to which the annual water yield under different conditions is used as the evaluation indicator for ecosystem water retention function [50]. It is calculated as follows [51]:

249 K WR “ Minp1, q ¨ Minp1, 0.3TIq ¨ Minp1, sat q ¨ WY (7) Velocity 300 where Velocity is the flow coefficient, TI is the topographic index, Ksat is the soil saturated hydraulic conductivity, and WY is the water yield, which is defined as the amount of water that runs off the landscape (precipitation minus storage and evapotranspiration losses) in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) program [52]. The formula for the water yield model is based on the Budyko curve and the annual average precipitation [53]: Sustainability 2016, 8, 594 6 of 17

AETxj WY “ 1 ´ P (8) xj P x ˆ x ˙ where WYxj is the annual water yield at pixel x of land cover type j, AETxj is the annual actual evapotranspiration for pixel x with land cover j, Px is the annual precipitation for pixel x, AETxj/Px is based on the expression of the Budyko curve [54] given as:

ω 1{ω AETxj PETxj PETxj “ 1 ` ´ 1 ` (9) P P P x x „ ˆ x ˙  where PETxj is the potential evapotranspiration for pixel x with land cover j, ω is a non-physical parameter that characterizes the natural climatic-soil properties, which is defined as:

AWCx ωx “ Z ` 1.25 (10) Px where AWCx is the volumetric (mm) plant available water content at pixel x, which is estimated using soil texture, soil depth, and root depth of vegetation [52,55], Z is the seasonal rainfall factor known as Zhang coefficient, which represents annual precipitation distribution and can be any real number in the range from 1 to 30. In our study area, we set Z to 7, according to the InVEST User’s Guide [52].

2.3.3. Correlations and Trade-offs Analysis Correlation analysis was used to estimate the influence of grassland degradation on the ESs. Pearson correlation coefficients between GDI and individual grassland ESs, including their variations, were calculated using ArcGIS 10.0 [41] and SPSS 19 [56] based on a random sample of 1000 points across the basin. Trade-offs and synergies are used to describe interactions between ESs. Trade-offs occur when one service increases in value and another one decreases, whereas synergies describe situations in which both services either increase or decrease [57]. These interactions could be identified by the correlation coefficients of ESs and their changes [58,59], where negative represents the trade-offs and positive represents the synergies.

3. Results

3.1. Spatiotemporal Patterns of GDI During 1983–2011 GDI exhibited the spatial gradient of decreasing from southeast to northwest on both sides of the Xilin River (Figure2). Mean GDI increased from 0.79 in 1983 to 0.99 in 2000, and then it decreased to 0.86 in 2011 (Figure3a). GDI in the upper reaches was lower than in the lower parts of the basin. The increase of GDI in 2000 was mostly due to its dramatic increases in the middle and lower reaches (Figures2 and3a). While GDI decreased slightly in 2011 it was still higher than in 1983 (Figure3a). Sustainability 2016, 8, 594 7 of 17 SustainabilitySustainability 2016 2016, ,8 8, ,594 594 7 of7 of17 17

FigureFigure 2. Spatial 2. Spatial distribution distribution of GDI of in GDI the in Xilin the River Xilin BasinRiver during Basin during 1983–2011. 1983–2011. (a), (b), ((ca),), and (b), ( (dc)), were Figureand (d 2.) were Spatial the distributionspatial distribution of GDI ofin GDIthe Xilinin 1983, River 1989, Basin 2000, during and 2011 1983–2011. respectively. (a), ( b), (c), theand spatial (d) distributionwere the spatial of GDI distribution in 1983, 1989, of GDI 2000, in and 1983, 2011 1989, respectively. 2000, and 2011 respectively.

Figure 3. Changes of GDI and ESs in the Xilin River Basin during 1983–2011. (a) was the changes of grassland degradation index during 1983–2011; (b), (c), and (d) were the changes Figure 3. Changes of GDI and ESs in the Xilin River Basin during 1983–2011. (a) was the Figureof aboveground 3. Changes of biomass; GDI and soil ESs conservation, in the Xilin Riverand water Basin retention during 1983–2011. respectively. (a) was the changes of grasslandchanges of degradation grassland degradation index during index 1983–2011; during (b 1983–2011;), (c), and ( d(b)), were (c), theand changes (d) were of the aboveground changes biomass;of aboveground soil conservation, biomass; and soil water conservation, retention respectively. and water retention respectively. Sustainability 2016, 8, 594 8 of 17

Sustainability 2016, 8, 594 8 of 17 3.2. Spatiotemporal Patterns of Grassland ESs 3.2. Spatiotemporal Patterns of Grassland ESs 3.2.1. Aboveground Biomass (AGB) 3.2.1. Aboveground Biomass (AGB) AGB generally decreases following the southeast-northwest spatial gradient (Figure4). Its temporalAGB dynamicsgenerally decreases generally following mirrored the that southeast of the‐ GDInorthwest by decreasing spatial gradient from about(Figure 707 4). kg/haIts to justtemporal 581.37 dynamics kg/ha in generally 2000 and mirrored increasing that toof the 775.06 GDI kg/ha by decreasing in 2011 from (Figure about3b). 707 Upper kg/ha sub-basinto just 581.37 kg/ha in 2000 and increasing to 775.06 kg/ha in 2011 (Figure 3b). Upper sub‐basin had had consistently higher AGB, while it was near equal in the middle and lower sub-basins, except in consistently higher AGB, while it was near equal in the middle and lower sub‐basins, except in 2000. 2000. According to long-term satellite data biomass in the basin has been decreasing during 1981–2015 According to long‐term satellite data biomass in the basin has been decreasing during 1981–2015 (Figure(Figures5a,c and5a,c and Figure 6a).6 However,a). However, a significant a significant increasing increasing trend can trend be seen can starting be seen from starting 2003 from despite 2003 despitethat thatprecipitation precipitation started started decreasing decreasing after 2008 after (Figure 2008 6). (Figure This is 6an). indication This is an of indication successful ofgrassland successful grasslandrestoration restoration in recent in years. recent years.

Figure 4. Spatial distribution of ESs in the Xilin River Basin during 1983–2011. Figure 4. Spatial distribution of ESs in the Xilin River Basin during 1983–2011. Sustainability 2016, 8, 594 9 of 17

3.2.2. Soil Conservation (SC) Spatial pattern of SC was very similar to AGB with low values in the northwest and high in the southeast (Figure4). It decreased significantly between 1983 and 2000 and restored slightly in 2011 with the pattern being consistent across the three sub-basins (Figure3c). The maximum amplitude of change in SC across space and time can reach 22 t/ha. Overall, SC decreased by 46.43%, from 18.50 t/ha in 1983 to 9.91 t/ha in 2011.

3.2.3. Water Retention (WR) WR generally repeats the spatial pattern of AGB and SC by increasing from the northwest to the southeast (Figure4). It did not show any trend by increasing from 4.43 mm in 1983 to 7.54 mm in 1989 and then decreasing sharply to 1.31 mm in 2000 and returning to 7 mm in 2011 (Figure3d). The maximum temporal amplitude of change in WR across sub-basins can be 9 mm.

3.3. Grassland Degradation and Changes in ESs at the Sub-basin Level GDI in all sub-basins first increased and then decreased during 1983–2011 (Table2, Figure3a). It was significantly higher in the middle and lower sub-basins. It peaked in the 1980s in the upper reaches and in the 1990s in the middle and lower sub-basins. This suggests degradation intensified first in the upper reaches and it was gradually picked up downstream in the next decade. This is further corroborated by the dynamics of the GDI rate of change (Table2).

Table 2. Change rate of GDI and ESs in the sub-basins during 1983–2011.

Change Rate (%) Sub-Basins 1983–1989 1989–2000 2000–2011 GDI AGB SC WR GDI AGB SC WR GDI AGB SC WR Upper reaches 49.15 ´3.45 ´36.68 49.25 ´22.36 ´9.02 ´15.07 ´71.64 ´4.05 32.89 12.49 245.34 Middle reaches 13.24 1.12 ´9.23 89.63 9.40 ´18.99 ´58.58 ´86.19 ´13.72 26.74 9.89 577.33 Lower reaches ´7.42 2.42 51.41 81.25 38.13 ´23.97 ´72.20 ´90.86 ´15.42 39.02 33.90 848.12 Whole basin 9.09 ´0.17 ´3.93 70.45 14.13 ´17.76 ´52.49 ´82.58 ´13.16 33.32 17.30 432.63 GDI: Grassland degradation index; AGB: Aboveground biomass; SC: Soil conservation; WR: Water retention.

ESs in sub-basins decreased between 1983 and 2000 and then increased afterwards (Figure3). The decrease was the fastest during 1989–2000. At the same time, the rate of change of ESs was high during this period with AGB, SC, and WR significantly decreasing (Table2). During 2000–2011, ESs have largely restored. However, the increase of SC was quite slow compared to that of AGB and WR, which suggests a time lag in soil restoration. Overall, grassland in the middle and lower reaches experienced more severe degradation than in upper reaches.

3.4. Relationships Between Grassland Degradation and ESs We found significant negative correlations between GDI and all grassland ESs (at 0.01 level) for individual years and between changes in GDI and AGB and SC (at 0.01 level). Correlations between changes in GDI and WR were positive but not significant. When ESs were compared with each other, AGB and SC, as well as their changes during the four time periods, all were positively correlated (at 0.01 level). AGB and SC were also positively correlated with WR while their changes were negative correlations with WR except for the period of 1989–2000 (Table3). Our findings suggest a distinct synergy between AGB and SC, while AGB and SC exhibited trade-offs with WR (Table3). Sustainability 2016, 8, 594 10 of 17

Table 3. Correlations of GDI and grassland ESs.

Year/Period GDI vs. AGB GDI vs. SC GDI vs. WR AGB vs. SC AGB vs. WR SC vs. WR 1983 ´0.488** ´0.354** ´0.312** 0.679** 0.492** 0.393** 1989 ´0.307** ´0.339** ´0.205** 0.732** 0.120* 0.209** 2000 ´0.706** ´0.592** ´0.389** 0.768** 0.543** 0.510** 2011 ´0.652** ´0.451** ´0.228** 0.762** 0.247** 0.306** 1983–1989 ´0.421** ´0.414** 0.075 0.848** ´0.051 ´0.015 1989–2000 ´0.582** ´0.539** 0.016 0.565** 0.027 0.196** 2000–2011 ´0.374** ´0.198** 0.049 0.733** ´0.144** ´0.051 1983–2011 ´0.153** ´0.143** 0.031 0.143** ´0.116* ´0.479** GDI: Grassland degradation index; AGB: Aboveground biomass; SC: Soil conservation; WR: Water retention. ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

4. Discussion

4.1. Impacts of Grassland Degradation and Restoration on ESs Our results show that grasslands in the Xilin River Basin experienced severe degradation during the 1980–90s, but they have been undergoing restoration since 2000. Degradation affected composition, structure, and ecosystem processes of grasslands, which diminished the provisioning and delivery of ESs. While precipitation has been found, the primary driver of spatial and temporal dynamics of biomass in grasslands of northern China [60–62], which is further confirmed by long-term satellite NDVI and precipitation data (Figure6), changes in land use policies may also have played an important role. The slope trends of the relationship between AGB and year were calculated in pixel scale across the study areas during 1981–2015. The results show that AGB in the area exhibited a weak decreasing trend (Figure5a,c and Figure6a). AGB significantly declined across 23.0% of the study area and increased significantly nearly 20.6% across of the study area. AGB did not change significantly during 1981–2015 in 56.4% of the study area (Figure5a,c). Although AGB in the area had a predominantly decreasing, although very weak trend, it changed to a significant increase after 2000 (Figure5b,d and Figure6a), which is largely attributed to the implementation of grassland restoration policies. AGB significantly increased across 6.1% of the study area and increased but not significantly nearly 81.7% across of the study area (Figure5b,d). AGB increase with improving grassland conditions is supported by correlations between GDI and AGB (Table3). Other recent studies of this region corroborate this finding by showing significant increases in AGB since 2000 [63,64]. Soil conditions are the critical resource for maintaining ESs in semi-arid environment of the Xilin River Basin. However, SC has declined significantly in the past 30 years. Currently, levels of soil degradation and have become one of the most serious ecological problems of Northern China [65]. While soil is at the core of the structure and function of ecosystems and delivery of essential ESs, which maintain the food production, water regulation, and carbon storage services [66], grassland soil conservation in Northern China has been significantly lacking [43]. Conservation efforts in the Xilin River Basin increased since 2000, although they showed less dynamics compared to sufficiently improved AGB and WR. There is clearly a time lag in soil improvement, which suggests that SC should be considered a key indicator of grassland restoration. Sustainability 2016, 8, 594 11 of 17 Sustainability 2016, 8, 594 11 of 17 Sustainability 2016, 8, 594 11 of 17

Figure 5. Trend analysis of AGB in the Xilin River Basin during 1981–2015. (a) and (b) were Figure 5. Trend analysis of AGB in the Xilin River Basin during 1981–2015. (a) and (b) were the spatial theFigure spatial 5. Trend distribution analysis of of the AGB slope in thetrends Xilin in Riveraboveground Basin during biomass 1981–2015. (AGB); (ca) and (db) were distribution of the slope trends in aboveground biomass (AGB); (c) and (d) were the proportion of the thethe proportionspatial distribution of the study of the area slope that trends significantly in aboveground increased, biomass increased (AGB); but not (c) significantly,and (d) were study area that significantly increased, increased but not significantly, decreased but not significantly, decreasedthe proportion but not of thesignificantly, study area and that significantly significantly decreased. increased, AGBincreased is based but onnot the significantly, maximum and significantly decreased. AGB is based on the maximum value composite of NOAA/AVHRR NDVI valuedecreased composite but not of significantly, NOAA/AVHRR and significantlyNDVI (1981–1999) decreased. and MODISAGB is basedNDVI on (2000–2015). the maximum (1981–1999) and MODIS NDVI (2000–2015). value composite of NOAA/AVHRR NDVI (1981–1999) and MODIS NDVI (2000–2015).

Figure 6. Changes of AGB and precipitation in the Xilin River Basin during 1981–2015. Figure 6. Changes of AGB and precipitation in the Xilin River Basin during 1981–2015. Figure 6. Changes of AGB and precipitation in the Xilin River Basin during 1981–2015. Sustainability 2016, 8, 594 12 of 17

WR is dependent upon vegetation cover. During the study periods, WR generally decreased, which can be attributed to changes in precipitation and evapotranspiration, the key factors determining the water supply of ecosystems according to the water balance principle [52]. The first factor reflects recent climate change in the area, and the second is directly linked to vegetation processes. During 1983–2011, annual precipitation decreased slightly in the area (Figure6b). Meanwhile, annual potential evapotranspiration also decreased in the past 30 years [67]. Changes in WR should therefore be considered in the context of both climate change and human activities, primarily human-induced changes in vegetation pattern, which has significant repercussions on evapotranspiration [68]. Several previous studies showed that vegetation cover in Xilin River Basin has changed significantly in the last 30 years [42,69,70]. As a result, evapotranspiration rates decreased and WR capacity greatly reduced. We conclude that changes in vegetation patterns play a significant role in maintaining the water cycle and hydrological processes in the basin.

4.2. Relationships between Grassland ESs Understanding the relationships between grassland ESs is helpful for developing grassland restoration strategies and ensuring environmental sustainability in arid and semiarid regions. Such relationships can also change with time and spatial scales. Studies have found that provisioning and regulating services always exhibit trade-offs at the regional scale, with growth in provisioning services leading to declines in regulating services [57–59,71]. However, our results demonstrate a synergy between provisioning (AGB) and regulating services (SC) (Table3), which may be driven by overgrazing and other human activities. It means that changes in AGB and SC share the same driving forces. More specifically, over the past three decades, land managers have maximized the consumption of provisioning services (fodder, meat, and milk) provided by grasslands in the basin with declines in AGB being an inevitable outcome. The decrease caused the reduction of regulating services (SC). The rational use of provisioning services is likely to slow the degradation and enhance regulating services. Our study found significant correlation between changes in ESs suggesting trade-offs between AGB and WR and between SC and WR (Table3) and confirming the dynamic nature of relationships between the ESs found previously [72]. We believe changes in vegetation cover due to successions have played an important role in these relationships. It is important to consider grassland ES trade-offs in management and restoration strategies in the future.

4.3. Drivers of Grassland Degradation Overgrazing and human population growth are the most important driving factors of grassland degradation in the Xilin River Basin. Such degradation is caused by multiple natural and human factors including long-term , wind erosion, water erosion, sandstorms, and overgrazing [6,73]. For the Xilin River Basin specifically, it has been shown that human activities (especially overgrazing) are the main factors of grassland degradation [6,8,9,35,74–76]. This is the result of rapid growth of and human population in the basin over the past three decades. Livestock in the basin exhibited a sharp increase during 1983–1999 and then decreased rapidly (Figure7). Human population also grew significantly by showing a 100% increase during 1983–2011 (Figure7). We also note a slight decrease in annual precipitation during 1983–2011 (Figure6b). Our findings of temporal patterns of GDI are consistent with the number of livestock during the study periods and confirm that, compared to precipitation, overgrazing played a greater role in grassland degradation during the last several decades. Sustainability 2016, 8, 594 13 of 17 that,Sustainability compared2016, 8 to, 594 precipitation, overgrazing played a greater role in grassland degradation during13 of 17 the last several decades.

Figure 7. 7.Population Population and and livestock livestock in thein the Xilin Xilin River River Basin Basin during during 1983–2011. 1983–2011. Data source: Data source:Xilinhot XilinhotCity Statistic City Yearbook Statistic(1980–2012) Yearbook (1980–2012) [77] (data are [77] taken (data at the are end taken of year). at the end of year).

Implementation of rational grassland management practices is expected to enhance grassland Implementation of rational grassland management practices is expected to enhance grassland ESs ESs and increase resilience of the arid and semiarid region, which has recently experienced and increase resilience of the arid and semiarid region, which has recently experienced , urbanization, cropland expansion, industrial development, and intensification of mining [42,78]. cropland expansion, industrial development, and intensification of mining [42,78]. Grasslands in Grasslands in the study area have restored since 2000 after experiencing two decades of severe the study area have restored since 2000 after experiencing two decades of severe degradation. It is degradation. It is a consequence of implementation of the Household Production Responsibility a consequence of implementation of the Household Production Responsibility System (HPRS), also System (HPRS), also known as the Double Contracts System in grassland areas. The HPRS was known as the Double Contracts System in grassland areas. The HPRS was launched in 1998 to deal with launched in 1998 to deal with grassland degradation caused by social and institutional problems in grassland degradation caused by social and institutional problems in the study area [79]. The policy the study area [79]. The policy was successful in controlling the number of livestock since 2000 (Figure was successful in controlling the number of livestock since 2000 (Figure7). In addition, other ecological 7). In addition, other ecological restoration programs, including the Grain for Green Project, the restoration programs, including the Grain for Green Project, the Beijing and Tianjin Sand Source Beijing and Tianjin Sand Source Control Project, and ecological compensation (payment for ESs) Control Project, and ecological compensation (payment for ESs) project, all have contributed to the project, all have contributed to the grassland restoration [80]. grassland restoration [80].

4.4. Uncertainties in Mapping and Modeling Grassland ESs We identify the following uncertainties in ES assessments in our study. The lack of previous ground data forced us to rely on regression modeling in estimating aboveground biomass biomass for for 1983, 1983, 1989, and 2000. The regression was constructed constructed using using AGB AGB data data from from 2011 2011 and and remote remote sensing sensing data. data. SC was was based based on on the the RUSLE RUSLE model, model, which which is a isstandard a standard method method to calculate to calculate soil loss, soil but loss, it has but several it has limitations.several limitations. The model The predicts model predicts erosion erosionfrom sheet from wash sheet alone wash (erosion alone (erosion from plains from in plains gentle in slopes) gentle [81].slopes) This [81 ].model This modeldoes doesnot include not include rills, rills, gullies, gullies, or or stream stream-bank‐bank erosion/deposition erosion/deposition processes. processes. Moreover, the model was developed for the Great Plains region in the US and was never tested in our study study area. area. Additionally, Additionally, individual individual effects effects of ofeach each variable variable are arenot notconsidered considered in the in model. the model. WR WRestimation estimation was wasbased based on the on water the water yield yield model model in InVEST in InVEST [50,51], [50,51 which], which calculates calculates water water cycling cycling in ain basin a basin without without considering considering human human consumption. consumption. Besides, Besides, surface surface water–groundwater water–groundwater interactions interactions are neglectedneglected entirely. entirely. We We therefore therefore conclude conclude that WR that estimation WR estimation may likely may be prone likely to be uncertainties. prone to uncertainties. 5. Conclusions 5. Conclusions To explore the relationship between ESs and grassland changes induced by vegetation succession, we calculatedTo explore the the degree relationship of grassland between degradation ESs and in thegrassland Xilin River changes Basin usinginduced GDI by in 1983,vegetation 1989, succession,2000, and 2011. we calculated AGB, SC, andthe WRdegree were of alsograssland estimated degradation to assess in ESs the for Xilin same River years. Basin Main using conclusions GDI in 1983,are as 1989, follows: 2000, (1) and GDI 2011. increased AGB, SC, during and 1983-2000WR were also and estimated decreased to during assess 2000–2011 ESs for same indicating years. Main that conclusionsgrasslands in are the as study follows: area (1) have GDI recently increased undergone during 1983 restoration‐2000 and after decreased experiencing during two 2000–2011 decades Sustainability 2016, 8, 594 14 of 17 of severe degradation. (2) Temporal dynamics of ESs were consistent with changes in GDI, which correlated negatively with AGB and SC. WR and SC are significantly affected by grassland conditions. They both declined in 1983–2000 and increased in the recent ten years. The increase of SC, however, was slower than that of AGB and WR, which suggests a time lag in soil restoration. (3) Grasslands in the middle and lower reaches have experienced severe degradation due to intensive human activities. (4) AGB and SC appeared to be synergistic ESs, while trade-offs existed between AGB and WR and between SC and WR. Overall, grassland ecosystems in the Xilin River Basin have changed considerably during the last three decades. Improving AGB should not be the only measure of grassland restoration. Other ESs, such as SC and WR, should be taken into account in the future.

Acknowledgments: We thank two anonymous reviewers for their valuable comments on an earlier version of the paper. This research was supported by the National Basic Research Program of China (Grant No. 2012CB722201), National Natural Science Foundation of China (Grant No. 31060320) and National Science and Technology Support Program (Grant No. 2011BAC07B01). Author Contributions: Conceived and designed the paper: Xuefeng Zhang, Jianming Niu. Collected, analyzed data, wrote the paper: Xuefeng Zhang. Improved language: Alexander Buyantuev, Jianming Niu. Partial data collection and processing: Qing Zhang, Jianjun Dong, Sarula Kang, Jing Zhang. Conflicts of Interest: The authors declare no conflict of interest.

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